Robotic D&D: Smart Robots: (Decontamination and Dismantling)

International audienceProject' managers continually seek an ever-greater optimization of time between remote handling operations and those carried out manually; therefore, new technological solutions must be deployed. Robotics offers a great opportunity in this new field of technology to carry out, for example, samplings or remediation in hostile, cluttered surroundings. Teams in charge of dismantling at the CEA have therefore first defined robotizable functions. These functions have been assembled from existing technological blocks to arrive at robots which are operating today [RICAIII, patent: FR 2925702]. Lessons learned, particularly from experience with the RICA robot, have enabled the operating technical specifications to be fine-tuned. A new study phase has been launched applying the same principle of adapting existing, proven means. The growing role of robotics today is unquestioned. Led by research and the academic world; robots such as those equipped with wheels, tracks, feet or even helicopter rotors, are today accessible to the general public, particularly via broadening of the " open source " concept. Added to these we need tools able to manage large component deconstruction systems, like MAESTRO. Industrialization of such high-potential technological solutions has been aided by:-Easy use,-Increasing reliability,-Flexibility of " open source " solutions,-Widening skill networks, and therefore greater technical support-Lower costs. Decontamination and dismantling (D&D) projects must be able to meet a number of special demands, increasing the number of unit designs, their costs and delivery times. The complexity of dismantling works sites mean that each is a special case to be dealt with almost independently. Such a way of approaching these projects is not on the same wavelength as industry, with tool and method standardization. The answer to the challenge of operations in difficult environments is an ecosystem of functions, performed by a set of interconnected robots. The first step towards the construction of such robot teams is devoted to functions where strength is not necessary: investigating and clean-up in hostile environments. With this in mind, the CEA Marcoule teams have been given the objective of merging the strengthening commercial robotic world with the needs of D&D, and thus to improve the transversal use of the systems

Robot humanoïde d'inspection et d'assainissement en boite gants nucléaire

Ce travail présente une évaluation de l'opportu-nité d'utiliser des robots humanoïdes en milieu nucléaire. Ce projet a utilisé la plateforme du DaRwIn-OP pour lui apporter les modifications nécessaires afin d'en faire un opérateur d'intervention en milieu nucléaire. Les deux axes de travail ont consisté à équiper l'humanoïde d'un capteur de mesure radiologique et d'une commande des bras par une caméra en champ profond. Les tests réalisés montrent la capacité de réaliser des mesures radiologiques au moyen du capteur intégré et la réalisation de frottis pour évaluer la contamination d'un objet

An humanoid robot for inspections and cleaning tasks in nuclear glove box

This article presents an opportunity evaluation of the use of humanoid robots in a nuclear environment. The project worked on the DaRwIn-OP platform to assess and carry out the modifications the robot needed to enable it to perform as an intervention operator in a nuclear location. The study had two main lines, based on equipping the humanoid with a radiological measurement capture system and with an arm command system using a depth camera. The tests performed showed the robot's ability to make radiological measurements with the built in detector and to collect swipe samples to assess the contamination of an object

Population dynamics of species-rich ecosystems: the mixture of matrix population models approach

Matrix population models are widely used to predict population dynamics, but when applied to species-rich ecosystems with many rare species, the small population sample sizes hinder a good fit of species-specific models. This issue can be overcome by assigning species to groups to increase the size of the calibration data sets. However, the species classification is often disconnected from the matrix modelling and from the estimation of matrix parameters, thus bringing species groups that may not be optimal with respect to the predicted community dynamics. We proposed here a method that jointly classified species into groups and fit the matrix models in an integrated way. The model was a special case of mixture with unknown number of components and was cast in a Bayesian framework. An MCMC algorithm was developed to infer the unknown parameters: the number of groups, the group of each species and the dynamics parameters. We applied the method to simulated data and showed that the algorithm efficiently recovered the model parameters. We applied the method to a data set from a tropical rain forest in French Guiana. The mixture matrix model classified tree species into well-differentiated groups with clear ecological interpretations. It also accurately predicted the forest dynamics over the 16-year observation period. Our model and algorithm can straightforwardly be adapted to any type of matrix model, using the life cycle diagram. It can be used as an unsupervised classification technique to group species with similar population dynamics. (Résumé d'auteur

Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model

Tree canopy height is one of the most important indicators of forest biomass, productivity, and species diversity, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the canopy height of all trees in the state of California with very high-resolution aerial imagery (60 cm) from the USDA-NAIP program. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP images collected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km$^2$ sites across various forest types and landscape variations in California. Our predictions of tree heights exhibited a mean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present in California. In 2020, trees taller than 5 m covered ~ 19.3% of California. Our model successfully estimated canopy heights up to 50 m without saturation, outperforming existing canopy height products from global models. The approach we used allowed for the reconstruction of the three-dimensional structure of individual trees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation and mapping capability, even in the presence of image distortion. These findings demonstrate the potential of large-scale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery.Comment: 29 pages, 9 figures, submitted to Remote Sensing in Ecology and Conservation (RSEC

Monitoring changes of forest height in California

Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities. Mapping and monitoring the structure of these forests at high spatial resolution provides the necessary data to better manage forest health, mitigate wildfire risks, and improve carbon sequestration. Here, we use LiDAR measurements of top of canopy height metric (RH98) from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission to map vegetation height across the entire California for two different time periods (2019–2020 and 2021–2022) and explore the impact of disturbance. Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. Model validation against independent airborne LiDAR data showed a R2≥0.65 for the top of canopy height outperforming existing GEDI-based height maps and with improved sensitivity for mapping tall trees (RH98 ≥ 50 m) across California. Height showed distinct spatial variations across forest types offering quantitative and spatial information to evaluate forest conditions. The model, trained on data from 2019 to 2020, showed a similar accuracy when applied to satellite imagery acquired in 2021–2022 allowing a robust detection of changes caused by natural and man-made disturbances of forest. Changes of height captured impacts of tree mortality and fire intensity, pointing to the influence of wildfire across landscapes. Fires caused more than 60% of the large forest disturbances between the two time periods. This study demonstrates the benefits of using locally trained ML models to rapidly modernize forest management techniques in the age of increasing climate risks

Diameter Growth Performance Varies with Species Functional-Group and Habitat Characteristics in Subtropical Rainforests

We examined tree diameter growth in 20 plots subjected to various disturbance intensities (natural, low, moderate and intensive logging) in a bid to understand the general tree growth responses in relation to habitat characteristics in subtropical rainforests of north-eastern New South Wales, Australia. Species-specific regeneration strategy, maximum size and level of shade tolerance were used to classify species into 5 groups; emergent and shade tolerant main canopy (group 1), shade tolerant mid canopy (2), shade tolerant understoreys (3), moderate shade tolerant (4), and shade intolerant (5) tree species. Data series for trees >10 cm diameter at 1.3 m above the ground level (dbh) providing observations spanning over 36 years were used in multilevel regression analyses. The results showed that spatial and temporal effects in tree growth at the stand-level are a combination of the differences between species functional group compositions and environmental gradients. High growth responses were observed in the shade intolerant species while increasing level of shade tolerance and decreasing maximum size decreased trees growth rates. Tree growth increased with altitude on a large scale across regions, and with disturbance intensity on a small scale at the plot (stand) level. Increase in northness (south through flat to north facing sites) increased growth in species group 1 for trees < 67 cm dbh, but beyond this dbh threshold the opposite was true. These showed that saplings of species group 1 may require increased illumination to reach the forest canopy, but once in the canopy, low soil water availability may be limiting to tree growth in the north facing sites. Decrease in northness was associated with increased growth in species group 2 indicating that reduced illumination and improved soil moisture in the south facing sites were conducive for maximum growth in this species group. Maximum growth potential in species group 4 and 5 increased with decrease in eastness, suggesting that the increased afternoon solar radiation and temperature were conducive for high growth rates in these species. Although topographic gradient may determine the spatial and temporal variations in tree growth where growth appeared to increase from the crest down the slope into the creek, its effects on soil fertility and water availability, and interactions between these and other factors may make it difficult to discern clear growth patterns